Array Camera Calibration Using Reference and Associate Imaging Components
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Solution Overview
Problem
Existing array camera calibration processes face challenges in balancing the need for precise data with minimal manufacturing test overhead, particularly in managing physical space and data storage requirements, while addressing distortions and variations among imaging components that affect super-resolution image fidelity and processing complexity.
Innovation Solution
A calibration method where one imaging component is designated as a reference, and others as associates, using a test pattern to generate scene-independent geometric corrections, colorimetric, and photometric corrections, with image data processing to align and correct for distortions, reducing the need for extensive physical space and data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration processes are used to measure MTF and collect characterization data, then measurement precision is improved, but device complexity and manufacturing test overhead increase
Solution Approach 1:
The calibration process is segmented into two distinct phases: a simplified factory calibration phase that captures essential parameters quickly, and a more comprehensive field calibration phase that can be performed later with full precision requirements. This segmentation allows manufacturing to proceed with minimal overhead while still achieving precise calibration ultimately.
Solution Approach 2:
Basic calibration data is collected during manufacturing as a preliminary action, establishing a baseline calibration. This preliminary calibration is sufficient for initial operation, and more precise calibration can be performed later in the field when precision requirements are more critical, thus reducing manufacturing complexity.
2Manufacturing precision
If comprehensive calibration data is collected for all imaging components, then manufacturing precision is improved, but loss of information (storage requirements) increases
Solution Approach 1:
The calibration process extracts only the essential calibration parameters needed for super-resolution imaging during manufacturing, storing minimal data. Additional comprehensive calibration data can be collected and stored later in the field when storage resources are more abundant and precision requirements are higher.
Solution Approach 2:
The calibration approach changes parameters based on operational context: during manufacturing, only essential parameters are measured and stored; during field operation, additional parameters can be measured and stored if needed, adapting the calibration depth to available resources and precision requirements.
3Reliability
If multiple imaging components are calibrated individually with full precision, then reliability is improved, but productivity decreases
Solution Approach 1:
The calibration process merges the calibration of multiple imaging components by establishing relative transformations between them based on captured images, rather than requiring separate full precision calibration for each component. This merging approach maintains reliability through consistent relative positioning while significantly improving manufacturing throughput.
Solution Approach 2:
A universal calibration approach is implemented where a single calibration process establishes relationships among all imaging components simultaneously. This multi-functional calibration method ensures consistent image quality across all components while maintaining high manufacturing productivity, as the same process calibrates the entire array rather than each component individually.
Data Source
AI summary
Systems and methods for calibrating an array camera are disclosed. Systems and methods for calibrating an array camera in accordance with embodiments of this invention include the capturing of an image of a test pattern with the array camera such that each imaging component in the array camera captures an image of the test pattern. The image of the test pattern captured by a reference imaging component is then used to derive calibration information for the reference component. A corrected image of the test pattern for the reference component is then generated from the calibration information and the image of the test pattern captured by the reference imaging component. The corrected image is then used with the images captured by each of the associate imaging components associated with the reference component to generate calibration information for the associate imaging components.